Version control is critical in mechanical computer-aided design (CAD) to enable traceability, manage product variation, and support collaboration. Yet, its implementation in modern CAD software as an essential information infrastructure for product development remains plagued by issues due to the complexity and interdependence of design data. This paper presents a systematic review of user-reported challenges with version control in modern CAD tools. Analyzing 170 online forum threads, we identify recurring socio-technical issues that span the management, continuity, scope, and distribution of versions. Our findings inform a broader reflection on how version control should be designed and improved for CAD and motivate opportunities for tools and mechanisms that better support articulation work, facilitate cross-boundary collaboration, and operate with infrastructural reflexivity. This study offers actionable insights for CAD software providers and highlights opportunities for researchers to rethink version control.

Parametric computer-aided design (CAD) models are constructed by a sequence of operations, where each operation may reference geometries created by earlier operations. This network of dependencies enables efficient modelling of complex geometry but also results in fragile models, where small modifications can trigger cascading errors. These interdependencies are obscured in commercial CAD systems, leaving users to rely on trial and error when navigating, modularizing, and debugging unfamiliar and complex models. In this paper, we motivate, present, and pilot CADModelScope, a multi-level graph-based visualization of operation dependencies integrated into a commercial CAD platform. In a qualitative lab study, we observed how participants locate and interpret operations, and how CADModelScope enhances awareness of hidden interdependencies and supports more structured navigation. Our findings highlight the potential of using the network of operation dependency as an effective representation for understanding and interacting with parametric CAD models, and we discuss implications for future tool design.

New developments to computer-aided design (CAD) software transform a once solitary modelling task into a collaborative one. The emerging multi-user CAD (MUCAD) systems allow virtual, real-time collaboration, with the potential to expand the learning outcomes and teaching methods of CAD. This paper proposes a MUCAD collaborative learning framework (MUCAD-CLF) to interpret backend analytic data from commercially available MUCAD software. The framework builds on several existing metrics from the literature and introduces newly developed methods to classify CAD actions collected from users’ analytic data. The framework contains two different classification approaches of user actions, categorizing actions by action type (e.g., creating, revising, viewing) and by design space (e.g., constructive, organizing), for comparative analysis. Next, the analytical framework is applied via a collaborative design challenge, corresponding to over 20,000 actions collected from 31 participants. Illustrative analyses utilizing the MUCAD-CLF are presented to demonstrate the resulting insight. Differences in CAD behaviour, indicating differences in learning, are observed between teams made up entirely of novices, entirely of experienced users, or a mix. In pairs of experts and novices, we see both a perceived high-satisfaction apprenticeship experience for the novices and preliminary evidence of an increase in expert design behaviours for the novices. The proposed framework is critical for MUCAD systems to make the most of the educational possibility of combining technical skill-building with team collaboration. Preliminary evidence collected in a fully-virtual design learning activity, and analyzed using the proposed MUCAD-CLF, shows that novice students gain advanced CAD design knowledge when collaborating with experienced teammates. With the user data captured by modern MUCAD software and the MUCAD-CLF presented herein, instructors and researchers can more efficiently assess and visualize students’ performance over the design learning process.